Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition

Aerolysin gene characterization and antimicrobial resistance profile of Aeromonas hydrophila isolated from milkfish (Chanos chanos) in Gresik, Indonesia

Background and Aim: Motile Aeromonas septicemia is a crucial disease in freshwater fish. Aeromonas hydrophila is a disease agent associated with sporadic fish mortality, food safety, and public health. This study aimed to estimate the prevalence and the presence of the aerolysin gene and antimicrobial resistance profile of A. hydrophila isolated from milkfish in Gresik, Indonesia. Materials and Methods: A total of 153 milkfish gill samples were collected from 16 locations in Gresik and then cultured and identified using biochemical tests. The aerolysin gene was investigated using a polymerase chain reaction, and antimicrobial resistance profiles of the recovered isolates were investigated. Results: Of the 153 examined samples, 35 (22.9%) were confirmed positive for A. hydrophila and 22 (62.9%) presented the aerolysin gene. The recovered isolates were resistant to the following antibiotics: Amoxicillin (62.9%), tetracycline (60%), streptomycin (54.3%), cefotaxime (51.4%), gentamycin (31.4%), kanamycin (28.6%), erythromycin (25.7%), chloramphenicol (20%), and trimethoprim (14.3%). Meanwhile, only ciprofloxacin, nalidixic acid, and imipenem were indicated as susceptible. Conclusion: The presence of the aerolysin gene is vital in determining the virulence of A. hydrophila. The study results indicated a high aerolysin gene prevalence. In addition, this study emphasized antibiotic use monitoring, food safety improvement, and negative impact reduction on human health and the environment.

Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells

The concept of hybrid drugs for targeting multiple aberrant pathways of cancer, by combining the key pharmacophores of clinically approved single-targeted drugs, has emerged as a promising approach for overcoming drug-resistance. Here, we report the design of unique hybrid molecules by combining the two pharmacophores of clinically approved BCR-ABL inhibitor (ponatinib) and HDAC inhibitor (vorinostat) and results of in vitro studies in drug-resistant CML cells. Robust 2D-QSAR and 3D-pharmacophore machine learning supervised models were developed for virtual screening of the hybrid molecules based on their predicted BCR-ABL and HDAC inhibitory activity. The developed 2D-QSAR model showed five information rich molecular descriptors while the 3D-pharmacophore model of BCR-ABL showed five different chemical features (hydrogen bond acceptor, donor, hydrophobic group, positive ion group, and aromatic rings) and the HDAC model showed four different chemical features (hydrogen bond acceptor, donor, positive ion group, and aromatic rings) for potent BCR-ABL and HDAC inhibition. Virtual screening of the 16 designed hybrid molecules identified FP7 and FP10 with better potential of inhibitory activity. FP7 was the most effective molecule with predicted IC₅₀ using the BCR-ABL based 2D-QSAR model of 0.005 μM and that of the HDAC model of 0.153 μM, and that using the BCR-ABL based 3D-pharmacophore model was 0.02 μM and that with HDAC model was 0.014 μM. In vitro study (dose-response relationship) of FP7 in wild type and imatinib-resistant CML cell lines harboring Thr315Ile or Tyr253His mutations showed growth inhibitory IC₅₀ values of 0.000 16, 0.0039, and 0.01 μM, respectively. This molecule also showed better biocompatibility when tested in whole blood and in PBMCs as compared to ponatinib or vorinostat.

Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R² = 0.8688, q² = 0.8600) and BACE1 (R² = 0.8177, q² = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r² = 0.8469, r²_pred = 0.8138) and BACE1 (r² = 0.7805, r²_pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules.

Simplex representation of molecular structure as universal QSAR/QSPR tool

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.

Ribosome-targeting antibacterial agents: Advances, challenges, and opportunities

Ribosomes, which synthesize proteins, are critical organelles for the survival and growth of bacteria. About 60% of approved antibiotics discovered so far combat pathogenic bacteria by targeting ribosomes. However, several issues, such as drug resistance and toxicity, have impeded the clinical use of ribosome-targeting antibiotics. Moreover, the complexity of the bacteria ribosome structure has retarded the discovery of new ribosome-targeting agents that are considered as the key to the drug-resistance and toxicity. To deal with these challenges, efforts such as medicinal chemistry optimization, combination treatment, and new drug delivery system have been developed. But not enough, the development of structural biology and new screening methods bring powerful tools, such as cryo-electron microscopy technology, advanced computer-aided drug design, and cell-free in vitro transcription/translation systems, for the discovery of novel ribosome-targeting antibiotics. Thus, in this paper, we overview the research on different aspects of bacterial ribosomes, especially focus on discussing the challenges in the discovery of ribosome-targeting antibacterial drugs and advances made to address issues such as drug-resistance and selectivity, which, we believe, provide perspectives for the discovery of novel antibiotics.

Pharmacophore-guided fragment-based design of novel mammalian target of rapamycin inhibitors: extra precision docking, fingerprint-based 2D and atom-based 3D-QSAR modelling

Rapamycin and their derivatives known as rapalogs were the first-generation mTOR inhibitors which interacted with mTORC1 but not with the mTORC2 protein. Second-generation inhibitors could bind with both and showed excellent anti-proliferative activity. Our aim was to design novel mTOR inhibitors which could bind at both the allosteric and the kinase site. The FRB domain is present in both the mTORC1 and mTORC2 protein complexes. We have employed e-pharmacophore-guided fragment-based design to develop novel mTOR inhibitors. The affinity of designed molecules at both the sites was analysed using molecular docking in extra precision mode. The atom-based 3D-QSAR model was developed to predict the activity while the fingerprint-based 2D-QSAR model was employed to refine an identified hit as potent dual mTOR inhibitor. Ligand ASK23 showed a docking score of -15.452 kcal/mol at the allosteric site (PDB ID 5GPG) while ASK38 showed a docking score of -11.535 kcal/mol at the kinase site (PDB ID 4JT6). Ligand ASK12 showed binding energy of -106.23 kcal/mol at the allosteric site. Refined molecule ASK12a from ASK12 showed the highest predicted activity (pIC₅₀: 6.512). The stability of the best hits and receptor complex was analysed using molecular dynamics simulation studies. Herein we report five potential mTOR dual inhibitors based on the predicted activity, drug-likeness analysis and off-target effects. To the best of our knowledge, this is the first report on pharmacophore-guided fragment-based drug design for mTOR inhibitors. This design strategy can be used for the rational drug design against other proteins for which only apo-structures are available. Communicated by Ramaswamy H. Sarma.

Novel tumor necrosis factor-α (TNF-α) inhibitors from small molecule library screening for their therapeutic activity profiles against rheumatoid arthritis using target-driven approaches and binary QSAR models

Tumor necrosis factor alpha (TNF-α) is a multifunctional cytokine that acts as a central biological mediator for critical immune functions, including inflammation, infection, and antitumor responses. It plays pivotal role in autoimmune diseases like rheumatoid arthritis (RA). The synthetic antibodies etanercept, infliximab, and adalimumab are approved drugs for the treatment of inflammatory diseases bind to TNF-α directly, preventing its association with the tumor necrosis factor receptor (TNFR). These biologics causes serious side effects such as triggering an autoimmune anti-antibody response or the weakening of the body's immune defenses. Therefore, alternative small-molecule based therapies for TNF-α inhibition is a hot topic both in academia and industry. Most of small-molecule inhibitors reported in the literature target TNF-α, indirectly. In this study, combined in silico approaches have been applied to better understand the important direct interactions between TNF-α and small inhibitors. Our effort executed with the extensive literature review to select the compounds that inhibit TNF-α. High-throughput structure-based and ligand-based virtual screening methods are applied to identify TNF-α inhibitors from 3 different small molecule databases (∼256.000 molecules from Otava drug-like green chemical collection, ∼ 500.000 molecules from Otava Tangible database, ∼2.500.000 Enamine small molecule database) and ∼240.000 molecules from ZINC natural products libraries. Moreover, therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles are also investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information, uses binary QSAR models. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular Dynamics (MD) simulations were also performed for selected hits to investigate their detailed structural and dynamical analysis beyond docking studies. As a result, at least one hit from each database were identified as novel TNF-α inhibitors after comprehensive virtual screening, multiple docking, e-Pharmacophore modeling (structure-based pharmacophore modeling), MD simulations, and MetaCore/MetaDrug analysis. Identified hits show predicted promising anti-arthritic activity and no toxicity. Communicated by Ramaswamy H. Sarma.